1. Field of the Invention
The present invention relates to the field of medical informatics, and more particularly to a method for using already existing clinical trial data to calculate figures for use in a new clinical trial.
2. Description of the Related Art
In the pharmaceutical industry, time to market is often the most important factor driving pharmaceutical profitability. In the U.S. alone, a huge percentage of total annual pharmaceutical research and development funds are spent on human clinical trials. Further, spending on clinical trials is growing with each passing year as trials increase both in number and complexity. A clinical trial refers to an investigation of safety and efficacy of a treatment for a disease or affliction, which treatment may include the use of drugs, counseling and/or other forms of therapy.
An analysis of the new treatment development process shows a major change in the drivers of time and cost. The discovery process, which formerly dominated time to market, has undergone a revolution due to techniques such as combinatorial chemistry and high-throughput screening. The regulatory phase has been reduced due to Federal Drug Administration (FDA) reforms and European Union harmonization. In their place, human clinical trials have become the main bottleneck. The time required for clinical trials accounts for a substantial amount of the time required for the average new treatment to come to market.
The conduct of clinical trials has changed remarkably little since trials were first performed. Clinical research remains largely a manual, labor-intensive, paper based process reliant on a cottage industry of physicians in office practices and academic medical centers. A typical clinical trial begins with the construction of a clinical protocol, a document which describes how a trial is to be performed, what data elements are to be collected, and what medical conditions need to be reported immediately to the pharmaceutical sponsor and the FDA. The clinical protocol and its authors are the ultimate authority on every aspect of the conduct of the clinical trial. This document is the basis for every action performed by multiple players in diverse locations during the entire conduct of the trial. Any deviations from the protocol specifications, no matter how well intentioned, threaten the viability of the data and its usefulness for an FDA submission.
The appropriate sample size of a clinical trial is a major component of the clinical protocol. Many other aspects of the clinical trial, including how the trial will be organized, how many health care providers are needed, the number of treatment centers required, and the number of countries involved depend on the sample size of the clinical trial. Further, the selection of an appropriate sample size is crucial to the outcome of the clinical trial. A sample size that is too small may fail to detect small treatment effects, but a sample size that is too large increases costs exponentially, thereby jeopardizing the completion and/or execution of the clinical trial.
Trials that evaluate the effect of treatments on survival are considered particularly important, not only because the outcome is so important, but also because the sample sizes are usually very large, and the trials very long. A trial to assess the ability of a drug to reduce blood pressure requires at most a few hundred patients, each observed for 8-12 weeks, while assessing the same drug's ability to reduce mortality might require 10,000 or more patients for 4-6 years. Survival trials can be used to evaluate not only a treatment's ability to extend time to death, but time to heart attack, cancer, development of AIDS, etc. The term “event” refers to the broader category that includes other outcomes such as heart attack, cancer, etc. in addition to “death”.
When statisticians design survival trials, they typically utilize survival curves from prior trials and record the readily available probability of surviving, say, at the end of those trials. They routinely ignore the wealth of information hidden in the entire survival curve, which is more difficult to extract.
Survival curves are a valuable way to summarize trial results, enabling clinicians to visualize cumulative effects at the end of the trial. However, those summaries do not reveal how the level of risk changed as the trial progressed. If patients enter a trial upon arriving in the emergency room after initial signs of a heart attack, initial risk might be quite high, diminishing as critical periods pass. If patients enter a different trial after their physicians discover increased blood pressure, the initial risk might be rather low, increasing as the patients age. Unlike the survival curve which shows only cumulative effects, the hazard curve shows how risk changes with time.
When trials of good treatments fail due simply to inadequate sample size, the costs for both society and the trial sponsor (usually a pharmaceutical company or the U.S. Federal Government) are extremely high. On the one hand, the treatment may erroneously appear ineffective, and development abandoned. Not only are all the time, effort and resources invested wasted, but patients who could benefit from the treatment may be denied life-saving therapy. Alternatively, the sponsor may still believe the treatment works. If the decision is that the trial should be re-run, this time with adequate sample size, the costs will be larger than the first time. But the biggest loss in this situation is the time necessary to get the new trial planned, initiated and completed. For a treatment with a billion dollar yearly revenue potential, such delays cost in excess of three million dollars each day. And these delays can last for years.
The presently available software tools in the pharmaceutical industry address various portions of the clinical protocol design process and the clinical trial process as a whole. In particular, software tools for calculating sample size are available. Some of these software tools allow users to enter time-dependent failure rates. Some allow a user to utilize a Markov model approach, while others allow a user to utilize simulation methods. None of the above software tools, however, address the issue of harnessing already existing clinical trial data to calculate an appropriate sample size for a new clinical trial.
Therefore, there is a need to overcome the deficiencies with the prior art and more particularly for a more effective way to calculate an appropriate sample size for a clinical trial using already existing clinical trial data.